Data in-lake verification method and system

Abstract

The invention discloses a data in-lake verification method and system, and belongs to the field of big data storage, and the method comprises the steps: comparing data structures of original data and migration data to obtain distinguishing information; selecting a verification strategy in combination with the distinguishing information, extracting data from the original data and the migration data according to the verification strategy, and comparing to obtain result information; the verification strategy comprises total comparison, layered random sampling comparison, row and column sampling comparison and graph random sampling comparison; the full-amount comparison comprises the steps of extracting all data items with at least one same data field for comparison; the layered random sampling comparison comprises the steps of extracting a part of data items with at least one same data field for comparison; the row and column sampling comparison comprises the step of extracting data items of the same row and / or the same column for comparison; the graph random sampling comprises the step of extracting data items of partial rows and / or partial columns in a preset graph coverage range for comparison. By comparing the structure and the content, the efficient verification of the data entering the lake is realized.

Description

Technical Field

[0001] The present application relates to the field of big data storage, and in particular to a method and system for verifying data entry into a lake. Background Art

[0002] In response to the requirements of enterprise digital transformation, all original data must be entered into the lake and written into the big data platform to obtain migration data, so as to carry out unified data governance, analysis, modeling, etc. Among them, both the original data and the migration data include multiple data items, each of which includes multiple data fields, and the data fields are divided into identity fields, collection time fields, and operation information fields. The field content of the identity field indicates the data source to which it belongs, the field content of the collection time field indicates the collection time, and the field content of the operation information field is the operation information of the data source to which it belongs.

[0003] Currently, after data is connected to the big data platform, the platform lacks the ability to verify data. How to determine whether the format of the migrated data is correct, whether information is missing, how to quickly compare a large amount of data, and if there is a problem with the migrated data, where the problem lies are all operations that need to be considered during the data migration process.

[0004] Currently, big data platforms do not have this function and all verification is done manually, which is inefficient and has incomplete verification coverage. Summary of the invention

[0005] The present application provides a data entry verification method and system, which can solve the technical problem of low efficiency of data entry verification in the prior art.

[0006] In a first aspect, an embodiment of the present application provides a data entry verification method, characterized in that the method comprises:

[0007] Compare the data structures of the original data and the migrated data to obtain distinguishing information;

[0008] A verification strategy is selected in combination with the distinguishing information, and data is extracted from the original data and the migrated data respectively according to the verification strategy and then compared to obtain result information; the verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison; the full comparison includes extracting all data items with at least one identical data field for comparison; the stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison; the row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison; the graphic random sampling includes extracting some rows and / or some columns of data items within the coverage range of a preset graphic for comparison.

[0009] In combination with the first aspect, in one implementation, comparing the data structures of the original data and the migrated data to obtain the difference information specifically includes the following steps:

[0010] Performing data structure analysis on the original data and the migrated data respectively to obtain first structure information and second structure information, wherein the first structure information and the second structure information both include data column information and data type information, and the second structure information also includes data distribution information; the data column information indicates the number of data fields in a data item, the data type information indicates the format type of data content filled in a data field, and the data distribution information indicates a storage location of a data item;

[0011] The first structure information and the second structure information are compared to obtain the difference information.

[0012] In combination with the first aspect, in one implementation, the step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps:

[0013] If the distinguishing information is that the number of columns in the original data is less than the number of columns in the migrated data, the data items that are not in the original data are deleted from the migrated data, and then a verification strategy is selected.

[0014] In combination with the first aspect, in one implementation, the step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps:

[0015] If the distinguishing information is that the number of rows in the original data is less than the number of rows in the migrated data, the original data and the migrated data are clustered according to the data items and then compared, and a verification strategy is selected according to the comparison result.

[0016] In combination with the first aspect, in one implementation, the step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps:

[0017] If the distinguishing information is that the format type of the data content in the original data is different from the format type of the data content in the previous data, the data content in the original data is associated with the data content in the migrated data, and then a verification strategy is selected.

[0018] In combination with the first aspect, in one implementation, obtaining verification result information according to the full amount comparison specifically includes the following steps:

[0019] All data items with the same data source and the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0020] In combination with the first aspect, in one implementation, obtaining verification result information according to stratified random sampling comparison specifically includes the following steps:

[0021] Partial data items with the same data source and / or the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0022] In combination with the first aspect, in one implementation, obtaining verification result information according to row and column sampling comparison specifically includes the following steps:

[0023] Extract data items with the same rows from the original data and the migrated data respectively for comparison to obtain result information; or

[0024] Extract data items with the same columns from the original data and the migrated data respectively for comparison to obtain result information; or

[0025] Data items with the same rows and columns are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0026] In combination with the first aspect, in one implementation, the preset graphics include a square shape, a T shape, and a diagonal line shape.

[0027] In a second aspect, an embodiment of the present application provides a data entry verification system, wherein the data entry verification device / system includes:

[0028] A data processing module, which is used to compare the data structures of the original data and the migrated data to obtain distinguishing information;

[0029] A data verification module is used to select a verification strategy in combination with distinguishing information, extract data from the original data and the migrated data according to the verification strategy, and then compare them to obtain result information; the verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison; the full comparison includes extracting all data items with at least one identical data field for comparison; the stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison; the row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison; the graphic random sampling includes extracting some rows and / or some columns of data items within the coverage of a preset graphic for comparison.

[0030] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0031] By comparing the data structures of the original data and the migrated data, we can obtain the difference information. According to whether there is a difference in the structure of the two data and the specific difference, we can choose the appropriate verification strategy. According to the verification strategy, we compare the original data and the migrated data to obtain the result information, so as to realize the verification of the migrated data entering the lake, and obtain the difference with the original data and the specific difference. The verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison. Different data structure differences are combined with different verification strategies to realize efficient verification of data entering the lake. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 This is a flow chart of an embodiment of a method for verifying data entry into a lake in this application;

[0033] Figure 2 This is a functional module diagram of an embodiment of the data entry verification system of this application. DETAILED DESCRIPTION

[0034] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0035] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0036] In a first aspect, an embodiment of the present application provides a method for verifying data entry into a lake.

[0037] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the data entry verification method of this application. Figure 1 As shown in the figure, the data entry verification method includes:

[0038] Step S1: Compare the data structures of the original data and the migrated data to obtain distinguishing information.

[0039] Step S2: select a verification strategy based on the distinguishing information, extract data from the original data and the migrated data according to the verification strategy, and compare them to obtain result information. The verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison.

[0040] The above-mentioned full comparison includes extracting all data items with at least one identical data field for comparison. The above-mentioned stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison. The above-mentioned row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison. The above-mentioned graphic random sampling includes extracting some rows and / or some columns of data items within the coverage of the preset graphic for comparison.

[0041] In this embodiment, the original data and the migration data are generally structured data, that is, each data item and the data fields and data content in each data item are presented in a table form, each row in the table is a data item, and each column in the table is a data field.

[0042] By comparing the data structures of the original data and the migrated data, we can obtain the difference information. According to whether there is a difference in the structure of the two data and the specific difference, we can choose the appropriate verification strategy. According to the verification strategy, we compare the original data and the migrated data to obtain the result information, so as to realize the verification of the migrated data entering the lake, and obtain the difference with the original data and the specific difference. The verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison. Different data structure differences are combined with different verification strategies to realize efficient verification of data entering the lake.

[0043] Furthermore, in one embodiment, the data structures of the original data and the migrated data are compared to obtain the difference information, which specifically includes the following steps:

[0044] The data structures of the original data and the migrated data are analyzed respectively to obtain first structure information and second structure information, wherein the first structure information and the second structure information both include data column information and data type information, and the second structure information also includes data distribution information. The data column information indicates the number of data fields in the data item, the data type information indicates the format type of the data content filled in the data field, and the data distribution information indicates the storage location of the data item.

[0045] The first structure information and the second structure information are compared to obtain the above-mentioned difference information.

[0046] In this embodiment, taking the case where both the original data and the migrated data are Excel file data as an example, the original data to be compared and the migrated data after migration are prepared, and some of the data are shown in Tables 1 and 2 below:

[0047] Table 1 List of some original data

[0048]

[0049] Table 2 Partial migration data list

[0050]

[0051]

[0052] The first structural information is extracted from the original data, and the first structural information includes that the original data list includes 5 columns, the number of original data rows is 1,000,000, and the number of SIM card numbers in the original data (the SIM card number is the data source identity of the data item) is 55. The first structural information also includes the format type of each data field content in the original data list.

[0053] The second structure information is extracted from the migration data, the second structure information includes that the migration data list includes 6 columns, the number of migration data rows is 999213 rows, and the number of SIM card numbers in the migration data is 55. The second structure information also includes the format type of each data field content in the migration data list. The second structure information also includes

[0054] By comparing the first structure information and the second structure information, the above-mentioned difference information is obtained, that is, the difference in data structure between the original data and the migrated data is obtained. Different data structures can select different verification strategies to efficiently and accurately find the difference in content between the original data and the migrated data.

[0055] For example, statistical field information: read the number of column names of the original data and the migrated data, which are 5 and 6 respectively. If they are different, the user is prompted that the number of fields is different and the migrated data has one more column "Partition".

[0056] Statistical data number: The number of data items read from the original data and the migrated data are 1,000,000 and 999,213 respectively. If they are different, the user is prompted that 787 data items are missing from the data migration.

[0057] Count the number of vehicles: that is, the number of SIM card numbers. The original data and the migration data are 55 and 55 respectively. If the numbers are the same, it means the number of vehicles is the same.

[0058] Further, in one embodiment, the above-mentioned selection of the verification strategy in combination with the distinguishing information specifically includes the following steps:

[0059] If the distinguishing information is that the number of columns in the original data is less than the number of columns in the migrated data, the data items that are not in the original data are deleted from the migrated data, and then a verification strategy is selected.

[0060] If the distinguishing information is that the number of rows in the original data is less than the number of rows in the migrated data, the original data and the migrated data are clustered according to the data items and then compared, and a verification strategy is selected according to the comparison result.

[0061] If the distinguishing information is that the format type of the data content in the original data is different from the format type of the data content in the previous data, the data content in the original data is associated with the data content in the migrated data, and then a verification strategy is selected.

[0062] In this embodiment, since 787 data items are missing in data migration, the distribution of the number of items on different dates is counted: that is, clustering statistics are performed based on "date". The number of data items after clustering the original data and the migrated data based on the date is shown in Table 3 below:

[0063] Table 3 List of data items after clustering the original data and migrated data according to date

[0064] date Raw data Migrate data 20240801 168683 168683 20240802 93039 93039 20240803 103583 103583 20240804 121961 121174 20240805 143874 143874 20240806 137498 137498 20240807 106332 106332 20240808 125030 125030

[0065] As can be seen from Table 3, the original data and the migrated data have the same distribution number except for 20240804, which has 787 missing migrated data.

[0066] According to the statistical information in 3, the data comparison is suitable for combined sampling, as shown in Table 4 below:

[0067] Table 4 List of combined sampling methods in the verification strategy

[0068]

[0069]

[0070] Further, in one embodiment, obtaining the verification result information based on the full amount comparison specifically includes the following steps:

[0071] All data items with the same data source and the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0072] In this embodiment, the specific operation of the full comparison is to remove the "partition" column, obtain the full information of the original data and the migrated data with a date of 20240804 for comparison, and the result is that except for the 787 missing data in the migrated data, the other data contents are the same.

[0073] Further, in one embodiment, the verification result information is obtained by comparing the stratified random sampling, and specifically includes the following steps:

[0074] Partial data items with the same data source and / or the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0075] In this embodiment, stratified random sampling is divided into stratified random sampling one and stratified random sampling two.

[0076] The specific operation of stratified random sampling 1 is to remove the "partition" column, customize the stratification algorithm, perform the first stratification of the data according to the "date", and then perform the second data stratification by the "sim card number". After that, 30% of the data of each vehicle is randomly extracted by the random sampling algorithm, and then the original data and migration data of the sample are compared. The result data content is the same. The operation example of stratified random sampling 1 is shown in Table 5 below:

[0077] Table 5 List of operational examples of stratified random sampling 1

[0078]

[0079] The specific operation of stratified random sampling 2 is to remove the "partition" column, customize the stratification algorithm, and stratify the data into four layers according to the date and partition columns: 20240801 and 1, 20240801 and 2, 20240802 and 0, 20240802 and 3. Then, 60% of the data is extracted through the random sampling algorithm, and then the original data and migration data of the sample are compared. The resulting data content is the same.

[0080] Further, in one embodiment, the verification result information is obtained by comparing the row and column sampling, and specifically includes the following steps:

[0081] Extract data items with the same rows from the original data and the migrated data respectively, compare them, and get the result information. Or

[0082] Extract data items with the same columns from the original data and the migrated data respectively, and compare them to obtain the result information. Or

[0083] Data items with the same rows and columns are extracted from the original data and the migrated data respectively for comparison to obtain result information.

[0084] In this embodiment, row and column sampling includes row random sampling, column random sampling, and row and column random sampling.

[0085] The specific operation of row random sampling is to sort all the data. The operation example of row random sampling is shown in Table 6 below. 400,000 rows of data are randomly obtained through row indexes, and bold data are extracted for comparison.

[0086] Table 6 List of random sampling operation examples

[0087]

[0088]

[0089] The specific operation of column random sampling is to sort all the data, sort the column names, randomly obtain 3 columns of data by column number, extract bold data for comparison, and the operation example of column random sampling is shown in Table 7 below.

[0090] Table 7 List of random sampling operation examples

[0091] 1 2 3 4 5 SIM card number Speed mileage Part Number date 12345678911 12 131 4j998 20240804 23456789012 30 200 4j099 20240801 34567890123 40 300 4j198 20240808 45678901234 50 400 4j298 20240808

[0092] The specific operation of random sampling of rows and columns is to sort all the data, sort the column names, randomly obtain the two columns of data of SIM card number and part number by column number, sample 40% of the data according to the row number, extract the bold data for comparison, and the operation examples of random sampling of rows and columns are shown in Table 8 below.

[0093] Table 8 List of operation examples of random sampling of rows and columns

[0094]

[0095] Furthermore, in one embodiment, the above-mentioned preset shapes include a square shape, a T shape, and a diagonal line shape.

[0096] In this embodiment, the random pattern sampling includes pattern sampling one, pattern sampling two, and random pattern sampling.

[0097] The specific operation of graphic sampling 1 is to sort all the data, and use the matrix sampling method to sample and extract the data according to the "mouth" graph, as shown in Table 9 below, and compare the bold data.

[0098] Table 9 List of operation examples of graphic sampling 1

[0099] SIM card number Speed mileage Part Number date 12345678911 12 131 4j998 20240804 23456789012 30 200 4j099 20240801 34567890123 40 300 4j198 20240808 45678901234 50 400 4j298 20240808

[0100] The specific operation of graphic sampling 2 is to sort all the data, and extract data sampling according to the "T" graph through the matrix sampling method, as shown in Table 10 below, and compare the bold data.

[0101] Table 10 List of operation examples of graphic sampling 2

[0102] SIM card number Speed mileage Part Number date 12345678911 12 131 4j998 20240804 23456789012 30 200 4j099 20240801 34567890123 40 300 4j198 20240808 45678901234 50 400 4j298 20240808

[0103] The specific operation of random graphic sampling is to sort all the data, randomly obtain 50% of the data through the row index, as shown in Table 11 below, extract data according to the matrix diagonal element formula according to the “\” graph (diagonal) rule, that is, bold the data, and then compare the bold data.

[0104] Table 11 List of operation examples of random graphic sampling

[0105]

[0106] According to the above comparison operation, the comparison results are shown in Table 12 below.

[0107] Table 12 Result Information List 1

[0108]

[0109]

[0110] The specific abnormal data are shown in Table 13 below.

[0111] Table 13 Result Information List 2

[0112]

[0113] From Tables 12 and 13 above, we can know the difference between the migrated data and the original data after the original data enters the lake, which facilitates the management of entering the lake.

[0114] On the second aspect, the embodiment of the present application also provides a data entry verification system.

[0115] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of an embodiment of the data entry verification device of this application. Figure 2 As shown, the data entry verification device includes:

[0116] The data processing module 1 is used to compare the data structures of the original data and the migrated data to obtain distinguishing information.

[0117] The data verification module 2 is used to select a verification strategy in combination with the distinguishing information, extract data from the original data and the migrated data according to the verification strategy, and then compare them to obtain result information. The above-mentioned verification strategies include full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison. The above-mentioned full comparison includes extracting all data items with at least one identical data field for comparison. The above-mentioned stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison. The above-mentioned row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison. The above-mentioned graphic random sampling includes extracting data items of some rows and / or some columns within the coverage range of the preset graphic for comparison.

[0118] In this embodiment, the original data and the migration data are generally structured data, that is, each data item and the data fields and data content in each data item are presented in a table form, each row in the table is a data item, and each column in the table is a data field.

[0119] By comparing the data structures of the original data and the migrated data, we can obtain the difference information. According to whether there is a difference in the structure of the two data and the specific difference, we can choose the appropriate verification strategy. According to the verification strategy, we compare the original data and the migrated data to obtain the result information, so as to realize the verification of the migrated data entering the lake, and obtain the difference with the original data and the specific difference. The verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison. Different data structure differences are combined with different verification strategies to realize efficient verification of data entering the lake.

[0120] Among them, the functional implementation of each module in the above-mentioned data entry into the lake verification system corresponds to the steps in the above-mentioned data entry into the lake verification method embodiment, and its functions and implementation processes will not be repeated here one by one.

[0121] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0122] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0123] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0124] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0125] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0126] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0127] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A data entry verification method, characterized in that: The method comprises: Compare the data structures of the original data and the migrated data to obtain distinguishing information; A verification strategy is selected in combination with the distinguishing information, and data is extracted from the original data and the migrated data respectively according to the verification strategy and then compared to obtain result information; the verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison; the full comparison includes extracting all data items with at least one identical data field for comparison; the stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison; the row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison; the graphic random sampling includes extracting some rows and / or some columns of data items within the coverage range of a preset graphic for comparison.

2. The data entry verification method according to claim 1, characterized in that: The data structures of the original data and the migrated data are compared to obtain difference information, which specifically includes the following steps: Performing data structure analysis on the original data and the migrated data respectively to obtain first structure information and second structure information, wherein the first structure information and the second structure information both include data column information and data type information, and the second structure information also includes data distribution information; the data column information indicates the number of data fields in a data item, the data type information indicates the format type of data content filled in a data field, and the data distribution information indicates a storage location of a data item; The first structure information and the second structure information are compared to obtain the difference information.

3. The data entry verification method according to claim 1, characterized in that: The step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps: If the distinguishing information is that the number of columns in the original data is less than the number of columns in the migrated data, the data items that are not in the original data are deleted from the migrated data, and then a verification strategy is selected.

4. The data entry verification method according to claim 1, characterized in that: The step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps: If the distinguishing information is that the number of rows in the original data is less than the number of rows in the migrated data, the original data and the migrated data are clustered according to the data items and then compared, and a verification strategy is selected according to the comparison result.

5. The data entry verification method according to claim 1, characterized in that: The step of selecting a verification strategy in combination with the distinguishing information specifically includes the following steps: If the distinguishing information is that the format type of the data content in the original data is different from the format type of the data content in the previous data, the data content in the original data is associated with the data content in the migrated data, and then a verification strategy is selected.

6. The data entry verification method according to claim 1, characterized in that: The verification result information is obtained according to the full comparison, specifically including the following steps: All data items with the same data source and the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

7. The data entry verification method according to claim 1, characterized in that: The verification result information is obtained by comparing the stratified random sampling, and specifically includes the following steps: Partial data items with the same data source and / or the same collection time are extracted from the original data and the migrated data respectively for comparison to obtain result information.

8. The data entry verification method according to claim 1, characterized in that: The verification result information is obtained by comparing the row and column sampling, and specifically includes the following steps: Extract data items with the same rows from the original data and the migrated data respectively for comparison to obtain result information; or Extract data items with the same columns from the original data and the migrated data respectively for comparison to obtain result information; or Data items with the same rows and columns are extracted from the original data and the migrated data respectively for comparison to obtain result information.

9. The data entry verification method according to claim 1, characterized in that: The preset graphics include a square shape, a T shape, and a diagonal line shape.

10. A data entry verification system, characterized in that: The data entry verification device / system comprises: A data processing module, which is used to compare the data structures of the original data and the migrated data to obtain distinguishing information; A data verification module is used to select a verification strategy in combination with distinguishing information, extract data from the original data and the migrated data according to the verification strategy, and then compare them to obtain result information; the verification strategy includes full comparison, stratified random sampling comparison, row and column sampling comparison, and graphic random sampling comparison; the full comparison includes extracting all data items with at least one identical data field for comparison; the stratified random sampling comparison includes extracting some data items with at least one identical data field for comparison; the row and column sampling comparison includes extracting data items of the same row and / or the same column for comparison; the graphic random sampling includes extracting some rows and / or some columns of data items within the coverage of a preset graphic for comparison.

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